Multicomponent and Longitudinal Imaging Seen as a Communication Channel—An Application to Stroke

Giacalone, Mathilde; Frindel, Carole; Grenier, Emmanuel; Rousseau, D.

doi:10.3390/e19050187

Cited by 6 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of course, adding spatiotemporal information will inevitably lead to a massive increase in data. Therefore, we selected the image information of the adjacent cerebral infarction area and obtained a more accurate prediction performance by aggregating local space and time, consistent with some studies ( Giacalone et al, 2017 ). Using regional image patches instead of the single-voxel method can predict cerebral infarction tissue fate and reduce data redundancy.…”

Section: Discussionmentioning

confidence: 81%

SWI and CTP fusion model based on sparse representation method to predict cerebral infarction trend

Wu,

Wang,

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

ObjectiveSWI image signal is related to venous reflux disorder and perfusion defect. Computed tomography perfusion (CTP) contains perfusion information in space and time. There is a complementary basis between them to affect the prognosis of cerebral infarction.MethodsSixty-six patients included in the retrospective study were designated as the training set. Effective perfusion indicator features and imaging radiomic features of the peri-infarction area on Susceptibility weighted imaging (SWI) and CTP modality images were extracted from each case. Thirty-three patients from the prospectively included group were designated as the test set of the machine learning model based on a sparse representation method. The predicted results were compared with the DWI results of the patients’ 7–10 days review to assess the validity and accuracy of the prediction.ResultsThe AUC of the SWI + CTP integrated model was 0.952, the ACC was 0.909, the SEN was 0.889, and the SPE was 0.933. The prediction performance is the highest. Compared with the value of AUC: the SWI model is 0.874, inferior to the performance of the SWI + CTP model, and the CTP model is 0.715.ConclusionThe prediction efficiency of the changing trend of infarction volume is further improved by the correlation between the combination of the two image features.

show abstract

Section: Discussionmentioning

confidence: 81%

SWI and CTP fusion model based on sparse representation method to predict cerebral infarction trend

Wu,

Wang,

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…In this paper, we propose an approach which, in addition to the clinical metadata, takes the MRI images as input. This is achieved by proposing a spatio-temporal encoding which has already proved its efficiency in previous works [10], [11]. However, unlike these previous works, this encoding is here specifically designed for deep learning architectures and is for the best of our knowledge presented for the first time in the context of images for stroke.…”

Section: Introductionmentioning

confidence: 99%

CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients

Hatami¹,

Cho²,

Mechtouff³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Clinical outcome prediction plays an important role in stroke patient management. From a machine learning point-of-view, one of the main challenges is dealing with heterogeneous data at patient admission, i.e. the image data which are multidimensional and the clinical data which are scalars. In this paper, a multimodal convolutional neural network -long short-term memory (CNN-LSTM) based ensemble model is proposed. For each MR image module, a dedicated network provides preliminary prediction of the clinical outcome using the modified Rankin scale (mRS). The final mRS score is obtained by merging the preliminary probabilities of each module dedicated to a specific type of MR image weighted by the clinical metadata, here age or the National Institutes of Health Stroke Scale (NIHSS). The experimental results demonstrate that the proposed model surpasses the baselines and offers an original way to automatically encode the spatio-temporal context of MR images in a deep learning architecture. The highest AUC (0.77) was achieved for the proposed model with NIHSS.Clinical relevance -We present the first deep learning approach predicting the clinical outcome of stroke patients treated by mechanical thrombectomy which integrates imaging data at the voxel level with key clinical metadata. Combining clinical and imaging data to evaluate the potential benefit from therapy closely mirrors the clinical decision process. Our promising results suggest our predictive model could assist in acute stroke management.

show abstract

“…The majority of the proposed methods for stroke lesion prediction only considers the standard parametric maps [19]. Only recently, perfusion DSC-MRI has been considered for final infarct stroke prediction [20]- [24]. Amorim et al…”

mentioning

confidence: 99%

Prediction of Stroke Lesion at 90-Day Follow-Up by Fusing Raw DSC-MRI With Parametric Maps Using Deep Learning

Pinto

Amorim

Hakim³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Stroke is the second most common cause of death in developed countries. Rapid clinical assessment and intervention have a major impact on preventing infarct growth and consequently on patients' quality of life. Clinical interventions aim to restore perfusion deficits via pharmaceutical or mechanical intervention. Regardless of which reperfusion procedure is used, clinicians need to consider the risks and benefits based on multi-modal neuroimaging studies, such as MRI scans, as well as their own clinical experience. This intricate decision-making process would benefit from an automatic prediction of the final infarct, which would provide a estimation of tissue that will probably infarct. This paper introduces a deep learning method to automatically predict ischemic stroke tissue outcome. The authors propose an end-to-end deep learning architecture that combines information from perfusion dynamic susceptibility MRI, alongside perfusion and diffusion parametric maps. We aim to automatically extract features from the raw perfusion DSC-MRI to further complement the information gleaned from standard parametric maps, and to overcome the loss of information that can occur during perfusion postprocessing. Combining both data types in a single architecture, with dedicated paths, we achieve competitive results when predicting the final stroke infarct core lesion in the publicly available ISLES 2017 dataset.

show abstract

Multicomponent and Longitudinal Imaging Seen as a Communication Channel—An Application to Stroke

Cited by 6 publications

References 31 publications

SWI and CTP fusion model based on sparse representation method to predict cerebral infarction trend

SWI and CTP fusion model based on sparse representation method to predict cerebral infarction trend

CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients

Prediction of Stroke Lesion at 90-Day Follow-Up by Fusing Raw DSC-MRI With Parametric Maps Using Deep Learning

Contact Info

Product

Resources

About